Inclusion of maintenance energy improves the intracellular flux predictions of CHO.

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times.

Towards rational glyco-engineering in CHO: from data to predictive models.

Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial.